摘要
利用递阶辨识原理、多新息辨识理论,研究和提出AR-OEARMA系统的辅助模型递阶广义增广随机梯度算法、辅助模型递阶多新息广义增广随机梯度算法、辅助模型递阶广义增广递推梯度算法、辅助模型递阶多新息广义增广递推梯度算法、辅助模型递阶广义增广最小二乘算法、辅助模型递阶多新息广义增广最小二乘算法。这些辅助模型递阶递推辨识方法可以推广到其他有色噪声干扰下的线性多变量和非线性多变量随机系统中。
For autoregressive output-error autoregressive moving average systems,this paper presents an auxiliary model hierarchical generalized extended stochastic gradient algorithm,an auxiliary model hierarchical multi-innovation generalized extended stochastic gradient al-gorithm,an auxiliary model hierarchical generalized extended recursive gradient algorithm,an auxiliary model hierarchical multi-innovation generalized extended recursive gradient algo-rithm,an auxiliary model hierarchical generalized extended least squares algorithm and an auxiliary model hierarchical multi-innovation generalized extended least squares algorithm by using the hierarchical identification principle and the multi-innovation identification theory.The proposed hierarchical identification methods can be extended to other linear and nonlin-ear multivariable stochastic systems with colored noises.
作者
丁锋
徐玲
籍艳
刘喜梅
DING Feng;XU Ling;JI Yan;LIU Ximei(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China;College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《青岛科技大学学报（自然科学版）》
CAS
2022年第2期1-13,共13页
Journal of Qingdao University of Science and Technology:Natural Science Edition
基金
国家自然科学基金项目(61873111).
关键词
参数估计
递推辨识
辅助模型辨识
多新息辨识
递阶辨识
最小二乘
随机系统
parameter estimation
recursive identification
auxiliary model identification
multi-innovation identification
hierarchical identification
least squares
stochastic system
